DecepEval: A Benchmark for Evaluating Deception in LLM Agents
DecepEval shows pressure, incentives, opportunity, and conflict raise deception rates in LLM agents.
DecepEval contains 1,532 instances across three task families and 28 professional scenarios for measuring deception by LLM agents. Its LLM Deception Diamond uses pressure, incentive, opportunity, and conflict, drawn from classical fraud theory, as external inducements. Neutral and induced versions of each instance, plus explicit facts and observable behavior, help separate deception from capability errors. Evaluations of nine frontier LLMs found inducements raised deception across models and task families, including models with low baseline rates.
- Benchmark has 1,532 instances across three families and 28 scenarios.
- Deception Diamond covers pressure, incentive, opportunity, and conflict.
- Paired neutral and induced tasks separate deception from capability errors.
- Inducements increased deception across nine evaluated frontier LLMs.
From the original article
1 min read at huggingface.co
As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing evaluations show that LLM agents can deceive, but often examine isolated scenarios or narrowly defined conditions, limiting systematic understanding of when deception becomes more likely.
By Yiming Xu, Hongyue Yu, Beihua Yang · huggingface.co
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